The estimation of local and structural mechanical properties of bones with micro Finite Element (microFE) models based on Micro Computed Tomography images depends on the quality bone geometry is captured, reconstructed and modelled. The aim of this study was to validate microFE models predictions of local displacements for vertebral bodies and to evaluate the effect of the elastic tissue modulus on model’s predictions of axial forces. Four porcine thoracic vertebrae were axially compressed in situ, in a step-wise fashion and scanned at approximately 39μm resolution in preloaded and loaded conditions. A global digital volume correlation (DVC) approach was used to compute the full-field displacements. Homogeneous, isotropic and linear elastic microFE models were generated with boundary conditions assigned from the interpolated displacement field measured from the DVC. Measured and predicted local displacements were compared for the cortical and trabecular compartments in the middle of the specimens. Models were run with two different tissue moduli defined from microindentation data (12.0GPa) and a back-calculation procedure (4.6GPa). The predicted sum of axial reaction forces was compared to the experimental values for each specimen. MicroFE models predicted more than 87% of the variation in the displacement measurements (R2 = 0.87–0.99). However, model predictions of axial forces were largely overestimated (80–369%) for a tissue modulus of 12.0GPa, whereas differences in the range 10–80% were found for a back-calculated tissue modulus. The specimen with the lowest density showed a large number of elements strained beyond yield and the highest predictive errors. This study shows that the simplest microFE models can accurately predict quantitatively the local displacements and qualitatively the strain distribution within the vertebral body, independently from the considered bone types.

The strain distribution in vertebral body has been measured in vitro in the elastic regime but only on the bone surface by means of strain gauges and digital image correlation. Digital volume correlation (DVC) based on micro‐computed tomography (micro‐CT) images allowed measurements of the internal strain distribution in bone at both tissue (trabecular and cortical bone) and organ (vertebra) levels. However, DVC has been mainly used to investigate failure of the vertebral body but has not yet been deployed to investigate the internal strain distribution in the elastic regime. The aim of this study was to investigate strain in the elastic regime and up to failure inside the vertebral body, including analysis of strain in all directions. Three porcine thoracic vertebrae were loaded in a step‐wise fashion at increasing steps of compression (5, 10 and 15%). Micro‐CT images were acquired at each step of compression. DVC successfully provided the internal strain distribution both in the elastic regime and up to failure. Micro‐CT images successfully identified regions of failure initiation and progression, which were well quantified by DVC‐computed strains. Interestingly, the same regions where failure eventually occurred experienced the largest strain magnitude also for the lowest degrees of compression (yet in the elastic regime).

Digital Volume Correlation (DVC) has become popular for measuring the strain distribution inside bone structures. A number of methodological questions are still open: the reliability of DVC to investigate augmented bone tissue, the variability of the errors between different specimens of the same type, the distribution of measurement errors inside a bone, and the possible presence of preferential directions. To address these issues, five augmented and five natural porcine vertebrae were subjected to repeated zero-strain micro-CT scan (39μm voxel size). The acquired images were processed with two independent DVC approaches (a local and a global one), considering different computation sub-volume sizes, in order to assess the strain measurement uncertainties. The systematic errors generally ranged within ±100 microstrain and did not depend on the computational sub-volume. The random error was higher than 1000 microstrain for the smallest sub-volume and rapidly decreased: with a sub-volume of 48 voxels the random errors were typically within 200 microstrain for both DVC approaches. While these trends were rather consistent within the sample, two individual specimens had unpredictably larger errors. For this reason, a zero-strain check on each specimen should always be performed before any in-situ micro-CT testing campaign. This study clearly shows that, when sufficient care is dedicated to preliminary methodological work, different DVC computation approaches allow measuring the strain with a reduced overall error (approximately 200 microstrain). Therefore, DVC is a viable technique to investigate strain in the elastic regime in natural and augmented bones.

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A retrospective study was conducted among Italian cancer healthcare workers (HCWs) to describe how influenza vaccination attitudes have changed during the COVID-19 pandemic. The analysis was conducted on the last three influenza seasons (2018/19, 2019/20 and 2020/21). To account for different relationships and proximity with patients, the study population was grouped into three main professional categories: health personnel, administrative staff and technicians. Moreover, to explore the factors affecting the coverage of influenza vaccine, a multinomial regression analysis was performed. Over the years, the influenza vaccination uptake showed a gradual increase across the overall staff, the highest coverage (53.8%) was observed in the season 2020/21, in particular, for health personnel (57.7%). In general, males resulted in more adherent to vaccination campaigns; nevertheless, this gap decreased in the last season. A total of 28.6% workers were always vaccinated throughout the past three seasons, a remarkable 25.2% (mainly young and females) received for the first time the influenza vaccination in 2020/21. In this dramatic health crisis, the attitudes of HCWs toward flu vaccination have changed. The COVID-19 outbreak increased adherence to flu vaccination, reaching the highest coverage in the campaign 2020/21. However, further efforts should be made to achieve greater vaccination coverage.